{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9fa62b70",
   "metadata": {},
   "source": [
    "### 逻辑斯蒂回归损失函数可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4feec11e",
   "metadata": {},
   "source": [
    "#### 导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edc39e32",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:41.609034Z",
     "start_time": "2022-06-14T00:21:41.593598Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.preprocessing import scale # 数据标准化Z-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcbc0e80",
   "metadata": {},
   "source": [
    "#### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f22f241c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:41.640949Z",
     "start_time": "2022-06-14T00:21:41.612008Z"
    }
   },
   "outputs": [],
   "source": [
    "# 1、加载乳腺癌数据\n",
    "data = datasets.load_breast_cancer()\n",
    "# 逻辑斯蒂回归，梯度下降，无量纲化处理：标准化\n",
    "X, y = scale(data['data'][:, :2]), data['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf28453",
   "metadata": {},
   "source": [
    "#### 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9fdbbf2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:41.671795Z",
     "start_time": "2022-06-14T00:21:41.644900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2、求出两个维度对应的数据在逻辑回归算法下的最优解\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a88d22",
   "metadata": {},
   "source": [
    "#### 方程系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "408c2a80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:41.686607Z",
     "start_time": "2022-06-14T00:21:41.678766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.33642177, -0.87670148]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4670f74c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:41.701863Z",
     "start_time": "2022-06-14T00:21:41.688511Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.336421768653383 -0.8767014817409343\n"
     ]
    }
   ],
   "source": [
    "# 3、分别把两个维度所对应的参数W1和W2取出来\n",
    "w1 = lr.coef_[0, 0]\n",
    "w2 = lr.coef_[0, 1]\n",
    "print(w1, w2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b62b3f56",
   "metadata": {},
   "source": [
    "#### sigmoid函数定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a35f622",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:41.716813Z",
     "start_time": "2022-06-14T00:21:41.703857Z"
    }
   },
   "outputs": [],
   "source": [
    "# 4、已知w1和w2的情况下，传进来数据的X，返回数据的y_predict\n",
    "# 预测的概率\n",
    "def sigmoid(X, w1, w2):\n",
    "    z = w1*X[0] + w2*X[1]\n",
    "    return 1 / (1 + np.exp(-z))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "689b5844",
   "metadata": {},
   "source": [
    "#### 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3419c87e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:41.732759Z",
     "start_time": "2022-06-14T00:21:41.720842Z"
    }
   },
   "outputs": [],
   "source": [
    "# 5、传入一份已知数据的X，y，如果已知w1和w2的情况下，计算对应这份数据的Loss损失\n",
    "def loss_function(X, y, w1, w2):\n",
    "    loss = 0\n",
    "    # 遍历数据集中的每一条样本，并且计算每条样本的损失，加到loss身上得到整体的数据集损失\n",
    "    for x_i, y_i in zip(X, y):\n",
    "        # 这是计算一条样本的y_predict，即概率\n",
    "        p = sigmoid(x_i, w1, w2)\n",
    "        loss += -1*y_i*np.log(p)-(1-y_i)*np.log(1-p)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd504a02",
   "metadata": {},
   "source": [
    "#### 最优解w1和w2，设置一个范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24505e05",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:42.875920Z",
     "start_time": "2022-06-14T00:21:41.737743Z"
    }
   },
   "outputs": [],
   "source": [
    "# 6、参数w1和w2取值空间\n",
    "w1_space = np.linspace(w1-2, w1+2, 100)\n",
    "w2_space = np.linspace(w2-2, w2+2, 100)\n",
    "\n",
    "loss1_ = np.array([loss_function(X, y, i, w2) for i in w1_space])\n",
    "loss2_ = np.array([loss_function(X, y, w1, i) for i in w2_space])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67c0f460",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:43.691434Z",
     "start_time": "2022-06-14T00:21:42.876872Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(100, 100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(100, 100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 7、数据可视化\n",
    "fig1 = plt.figure(figsize=(12, 9))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(w1_space, loss1_)\n",
    "plt.xlabel('w1 VS loss')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(w2_space, loss2_)\n",
    "plt.xlabel('w2 VS loss')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "w1_grid, w2_grid = np.meshgrid(w1_space, w2_space) # 网格交叉\n",
    "display(w1_space.shape,w2_space.shape,w1_grid.shape,w2_grid.shape)\n",
    "loss_grid = loss_function(X, y, w1_grid, w2_grid)\n",
    "\n",
    "plt.contour(w1_grid, w2_grid, loss_grid,20) # 等高线\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.contourf(w1_grid, w2_grid, loss_grid,20) # 等高面\n",
    "plt.savefig('./4-损失函数可视化.png',dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3645f09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:43.707355Z",
     "start_time": "2022-06-14T00:21:43.694399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[1, 2, 3],\n",
       "        [1, 2, 3]]),\n",
       " array([[4, 4, 4],\n",
       "        [6, 6, 6]])]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([1,2,3])\n",
    "B = np.array([4,6])\n",
    "\n",
    "np.meshgrid(A,B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ff9c359",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:44.807901Z",
     "start_time": "2022-06-14T00:21:43.711049Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8、3D立体可视化\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "fig2 = plt.figure(figsize=(12,6))\n",
    "ax = Axes3D(fig2)\n",
    "\n",
    "ax.plot_surface(w1_grid, w2_grid, loss_grid,cmap = 'viridis')\n",
    "\n",
    "plt.xlabel('w1',fontsize = 20)\n",
    "plt.ylabel('w2',fontsize = 20)\n",
    "ax.view_init(30,-30) # 调整视图角度\n",
    "plt.savefig('./5-损失函数可视化.png',dpi = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec10550",
   "metadata": {},
   "source": [
    "### PCA降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6f3017a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:44.823241Z",
     "start_time": "2022-06-14T00:21:44.811905Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# decomposition 降解，降维，分解【由多变少】\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53883755",
   "metadata": {},
   "source": [
    "#### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f278205",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:44.852956Z",
     "start_time": "2022-06-14T00:21:44.827231Z"
    }
   },
   "outputs": [
    {
     "ename": "InvalidParameterError",
     "evalue": "The 'return_X_y' parameter of load_iris must be an instance of 'bool' or an instance of 'numpy.bool_'. Got 3 instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X,y \u001b[38;5;241m=\u001b[39m \u001b[43mdatasets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_iris\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreturn_X_y\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m display(X\u001b[38;5;241m.\u001b[39mshape,y\u001b[38;5;241m.\u001b[39mshape)\n",
      "File \u001b[1;32md:\\developer\\python\\396\\lib\\site-packages\\sklearn\\utils\\_param_validation.py:203\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    200\u001b[0m to_ignore \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcls\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    201\u001b[0m params \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m params\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m to_ignore}\n\u001b[1;32m--> 203\u001b[0m \u001b[43mvalidate_parameter_constraints\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    204\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparameter_constraints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaller_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__qualname__\u001b[39;49m\n\u001b[0;32m    205\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    211\u001b[0m         )\n\u001b[0;32m    212\u001b[0m     ):\n",
      "File \u001b[1;32md:\\developer\\python\\396\\lib\\site-packages\\sklearn\\utils\\_param_validation.py:95\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[1;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     90\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     91\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     92\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     93\u001b[0m     )\n\u001b[1;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     98\u001b[0m )\n",
      "\u001b[1;31mInvalidParameterError\u001b[0m: The 'return_X_y' parameter of load_iris must be an instance of 'bool' or an instance of 'numpy.bool_'. Got 3 instead."
     ]
    }
   ],
   "source": [
    "X,y = datasets.load_iris(return_X_y=3)\n",
    "display(X.shape,y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbdacb2",
   "metadata": {},
   "source": [
    "#### PCA降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b1d239c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:44.883328Z",
     "start_time": "2022-06-14T00:21:44.856704Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m pca \u001b[38;5;241m=\u001b[39m PCA(n_components\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, \u001b[38;5;66;03m# 选择两个特征\u001b[39;00m\n\u001b[0;32m      2\u001b[0m           whiten\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;66;03m# 归一化\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m pca\u001b[38;5;241m.\u001b[39mfit(\u001b[43mX\u001b[49m) \u001b[38;5;66;03m# 训练，计算过程\u001b[39;00m\n\u001b[0;32m      4\u001b[0m X_pca \u001b[38;5;241m=\u001b[39m pca\u001b[38;5;241m.\u001b[39mtransform(X) \u001b[38;5;66;03m# 转变\u001b[39;00m\n\u001b[0;32m      5\u001b[0m display(X[:\u001b[38;5;241m5\u001b[39m],X_pca[:\u001b[38;5;241m5\u001b[39m],X_pca\u001b[38;5;241m.\u001b[39mmean(axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m),X_pca\u001b[38;5;241m.\u001b[39mstd(axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=2, # 选择两个特征\n",
    "          whiten=True) # 归一化\n",
    "pca.fit(X) # 训练，计算过程\n",
    "X_pca = pca.transform(X) # 转变\n",
    "display(X[:5],X_pca[:5],X_pca.mean(axis = 0),X_pca.std(axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e2582ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:44.915088Z",
     "start_time": "2022-06-14T00:21:44.887383Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m pca \u001b[38;5;241m=\u001b[39m PCA(n_components\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.97\u001b[39m, \u001b[38;5;66;03m# 选择特征权重大于95%\u001b[39;00m\n\u001b[0;32m      2\u001b[0m           whiten\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;66;03m# 归一化\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m pca\u001b[38;5;241m.\u001b[39mfit(\u001b[43mX\u001b[49m) \u001b[38;5;66;03m# 训练，计算过程\u001b[39;00m\n\u001b[0;32m      6\u001b[0m X_pca \u001b[38;5;241m=\u001b[39m pca\u001b[38;5;241m.\u001b[39mtransform(X) \u001b[38;5;66;03m# 转变\u001b[39;00m\n\u001b[0;32m      7\u001b[0m display(X[:\u001b[38;5;241m5\u001b[39m],X_pca[:\u001b[38;5;241m5\u001b[39m],X_pca\u001b[38;5;241m.\u001b[39mmean(axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m),X_pca\u001b[38;5;241m.\u001b[39mstd(axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=0.97, # 选择特征权重大于95%\n",
    "          whiten=True) # 归一化\n",
    "\n",
    "pca.fit(X) # 训练，计算过程\n",
    "\n",
    "X_pca = pca.transform(X) # 转变\n",
    "display(X[:5],X_pca[:5],X_pca.mean(axis = 0),X_pca.std(axis = 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b134cd55",
   "metadata": {},
   "source": [
    "#### 模型训练【非降维数据】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa6ae6bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:44.931196Z",
     "start_time": "2022-06-14T00:21:44.920968Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb77f46f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:44.979298Z",
     "start_time": "2022-06-14T00:21:44.937185Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X_train,X_test,y_train,y_test \u001b[38;5;241m=\u001b[39m train_test_split(\u001b[43mX\u001b[49m,y,test_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.2\u001b[39m,random_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m)\n\u001b[0;32m      2\u001b[0m model \u001b[38;5;241m=\u001b[39m LogisticRegression()\n\u001b[0;32m      3\u001b[0m model\u001b[38;5;241m.\u001b[39mfit(X_train,y_train)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state = 10)\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc15c7bc",
   "metadata": {},
   "source": [
    "#### 模型训练【降维数据】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "81191e63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-14T00:21:45.011344Z",
     "start_time": "2022-06-14T00:21:44.982294Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_pca' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X_train,X_test,y_train,y_test \u001b[38;5;241m=\u001b[39m train_test_split(\u001b[43mX_pca\u001b[49m,y,test_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.2\u001b[39m,random_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1024\u001b[39m)\n\u001b[0;32m      2\u001b[0m display(X_train\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m      3\u001b[0m model \u001b[38;5;241m=\u001b[39m LogisticRegression()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X_pca' is not defined"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X_pca,y,test_size = 0.2,random_state = 1024)\n",
    "display(X_train.shape)\n",
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba94cf3d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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